Computer Vision Based Method for Extracting Features Relating to the Developmental Stages of Trichuris Spp. Eggs

1. A computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs, said method comprising: a) obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution, b) detecting one or more Trichuris spp. eggs in the image(s), and c) extracting one or more features from an egg content image region representing at least part of the egg contents of a detected egg, wherein one or more features are extracted from an egg content image region being extracted from an image or image region which includes a full representation of a detected Trichuris spp. egg, and wherein the extracted egg content image region excludes the polar plugs of the detected Trichuris spp. egg.

2. A method according to claim 1 , wherein the extracted egg content image region excludes the shell of the detected Trichuris spp. egg.

3. A method according to claim 2 , wherein the extracted egg content image region has a substantially elliptical shape, thereby defining a content ellipse image.

4. A computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs, said method comprising: a) obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution, b) detecting one or more Trichuris spp. eggs in the image(s), and c) extracting one or more features from an egg content image region representing at least part of the egg contents of a detected egg wherein one or more features are extracted from an egg content image region being a bright-field egg content image region, and wherein the bright-field egg content image region is extracted from a bright-field image or image region, which includes a full representation of a detected Trichuris spp. egg, with the extracted egg content image region excluding the polar plugs of the detected Trichuris spp. egg.

5. A method according to claim 4 , wherein the extracted egg content image region excludes the shell of the detected Trichuris spp. egg.

6. A method according to claim 5 , wherein the extracted egg content image region has a substantially elliptical shape, thereby defining a content ellipse image.

7. A computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs, said method comprising: a) obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution, b) detecting one or more Trichuris spp. eggs in the image(s), and c) extracting one or more features from an egg content image region representing at least part of the egg contents of a detected egg, wherein the extraction of one or more features from the egg content image region includes one or more measurements of the direction-dependent structures of the egg contents.

8. A method according to claim 7 , wherein the extraction of one or more features from the egg content region includes one or more measurements of the longitudinal structures of the egg contents and/or one or more measurements of the transverse structures of the egg contents.

9. A method according to claim 8 , wherein the one or more measurements of the longitudinal structures are based on a measure of the linear structures and/or edge structures in the longitudinal direction.

10. A method according to claim 9 , wherein the linear structures and/or edge structures are measured at a predetermined scale.

11. A method according to claim 9 , wherein one or more measurements of the transverse structures are based on a measure of the linear structures and/or edge structures in the transverse direction, and wherein expressions representing a measure of the edge structures in the longitudinal and transverse directions are obtained by use of an edge detector algorithm.

12. A method according to claim 11 , wherein the edge detector algorithm is selected from the following algorithms: the Canny edge detector algorithm, the Sobel edge detector algorithm, and the Prewitt edge detector algorithm.

13. A method according to claim 11 , wherein the expression representing the edge structures in the longitudinal direction, longitudinal edge count, is defined as the number of edge pixels of the egg contents given by the edge detector algorithm and being oriented substantially in the longitudinal direction, and wherein the expression representing the edge structures in the transverse direction, transverse edge count, is defined as the number of edge pixels of the egg contents given by the edge detector algorithm and being oriented substantially in the transverse direction.

14. A method according to claim 13 , wherein the longitudinal edge count is defined as the number of edge pixels of the egg contents given by the edge detector algorithm and being oriented in the longitudinal direction plus/minus an angle within the range of 10-45 degrees, and wherein the transverse edge count is defined as the number of edge pixels of the egg contents given by the edge detector algorithm and being oriented in the transverse direction plus/minus an angle within the range of 10-45 degrees.

15. A method according to claim 8 , wherein one or more measurements of the transverse structures are based on a measure of the linear structures and/or edge structures in the transverse direction.

16. A method according to claim 15 , wherein the linear structures and/or edge structures are measured at a predetermined scale.

17. A method according to claim 8 , wherein the one or more measurements of the longitudinal structures are based on a measure of the linear structures and/or edge structures in the longitudinal direction at one or more scales in a multi-scale representation of the image region from which the features are extracted.

18. A method according to claim 17 , wherein the multi-scale representation of the image region from which the features are extracted is a pyramid representation or a scale space representation.

19. A method according to claim 8 , wherein one or more measurements of the transverse structures are based on a measure of the linear structures and/or edge structures in the transverse direction at one or more scales in a multi-scale representation of the image region from which the features are extracted.

20. A method according to claim 19 , wherein the multi-scale representation of the image region from which the features are extracted is a pyramid representation or a scale space representation.

21. A method according to claim 8 , wherein one or more measurements of the longitudinal structures of the egg contents is based on a longitudinal comparison of pixels intensities obtained from similarly addressed pixels in first and second image parts representing at least part of the egg contents of a detected egg, with the second image part being obtained by shifting the first image part one or more pixels in a direction substantially following the longitudinal direction of the egg.

22. A method according to claim 21 , wherein one or more measurements of the transverse structures of the egg contents is based on a transverse comparison of pixel intensities obtained from similarly addressed pixels in the first image part and a third image part representing at least part of the egg contents of a detected egg, with the third image part being obtained by shifting the first image part one or more pixels in a direction substantially following the transverse direction of the egg, and wherein the longitudinal comparison of pixel intensities from the first and second image parts comprises calculating a longitudinal correlation coefficient ρ long for pixel intensities representing at least part of the similarly addressed pixels, and wherein the transverse comparison of pixel intensities from the first and third image parts comprises calculating a transverse correlation coefficient ρ trans for pixel intensities representing at least part of the similarly addressed pixels.

23. A method according to claim 22 , wherein the feature extraction further includes a ratio measure based on the ratio between the longitudinal correlation coefficient ρ long and the transverse correlation coefficient ρ trans .

24. A method according to claim 8 , wherein one or more measurements of the transverse structures of the egg contents is based on a transverse comparison of pixel intensities obtained from similarly addressed pixels in the first image part and a third image part representing at least part of the egg contents of a detected egg, with the third image part being obtained by shifting the first image part one or more pixels in a direction substantially following the transverse direction of the egg.

25. A computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs, said method comprising: a) obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution, the stored digital images of the Trichuris spp. eggs comprising one or more dark-field images, b) detecting one or more Trichuris spp. eggs in the image(s), and c) extracting one or more features from an egg content image region representing at least part of the egg contents of a detected egg, wherein one or more features are extracted from an egg content image region being a dark-field egg content image region.

26. A method according to claim 25 , wherein one or more features are extracted from a dark-field egg content image region being extracted from a dark-field image region which includes a full representation of a detected Trichuris spp. egg.

27. A method according to claim 26 , wherein the extracted dark-field egg content image region excludes the polar plugs of the detected Trichuris spp. egg.

28. A method according to claim 27 , wherein the extracted dark-field egg content image region excludes the shell of the detected Trichuris spp. egg.

29. A method according to claim 28 , wherein the extracted dark-field egg content image region has a substantially elliptical shape, thereby defining a content ellipse image.

30. A method according to claim 26 , wherein the dark-field feature extraction is based on variations in pixel intensities measured or extracted for at least part of the dark-field egg content image region.

31. A method according to claim 30 , wherein the dark-field feature extraction comprises a computation of the average of the extracted pixel intensities.

32. A method according to claim 30 , wherein the dark-field feature extraction comprises a computation of the mean of the extracted pixel intensities, mean scattering intensity, and/or of the median of the extracted pixel intensities, median scattering intensity.

33. A computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs, said method comprising: a) obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution, b) detecting one or more Trichuris spp. eggs in the image(s), c) extracting one or more features from an egg content image region representing at least part of the egg contents of a detected egg, and d) classifying the detected egg based on at least part of the features extracted from the egg content image region representing the detected egg, the classification of the detected egg is a binary classification with respect to the developmental stage of the egg.

34. A method according to claim 33 , wherein the detected egg is classified as either containing a larva or not containing a larva.

35. A method according to claim 33 , wherein the classification is at least partly based on extracted dark-field features.

36. A computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs, said method comprising: a) obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution, b) detecting one or more Trichuris spp. eggs in the image(s), c) extracting one or more features from an egg content image region representing at least part of the egg contents of a detected egg, and d) classifying the detected egg based on at least part of the features extracted from the egg content image region representing the detected egg, the classification of the detected egg is a multi-class classification with respect to the developmental stage of the egg, said multi-class classification comprising at least three classes of developmental stages.

37. A computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs, said method comprising: a) obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution, b) detecting one or more Trichuris spp. eggs in the image(s), c) extracting one or more features from an egg content image region representing at least part of the egg contents of a detected egg, and d) classifying the detected egg based on at least part of the features extracted from the egg content image region representing the detected egg, the classification is at least partly based on extracted features, for which features the extraction includes one or more measurements representing longitudinal structures and transverse structures of the egg contents.

38. A method according to claim 37 , wherein the classification is at least partly based on a ratio measure obtained from a measure representing the longitudinal structures of the egg contents and a measure representing the transverse structures of the egg contents.

39. A method according to claim 38 , wherein one or more measurements representing the longitudinal structures are based on a measure of the linear structures and/or edge structures in the longitudinal direction, and wherein one or more measurements representing the transverse structures are based on a measure of the linear structures and/or edge structures in the transverse direction.

40. A method according to claim 37 , wherein a measure representing the longitudinal structures of the egg contents have to exceed a corresponding measure representing the transverse structures of the egg contents by a predetermined factor being larger than one in order to have the egg classified as containing a larva.

41. A computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs, said method comprising the steps of: a) obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution on a computer, b) executing instructions on the computer to detect one or more Trichuris spp. Eggs in the image(s), c) executing instructions on the computer to extract one or more features from an egg content image region representing at least part of the egg contents of a detected egg, and d) executing instructions on the computer to determine or classify at least part of the detected egg, for which part one or more features are extracted, as containing a larva or not, or to determine the developmental stage of the egg and use the determination or classification results as an indicator of the biological potency of the egg.

42. A method according to claim 41 , further comprising the step of generating a report based at least partly on the determination or classification results obtained in step d.